# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
import numpy as np
import torch
import torch.amp as amp
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin
from diffusers.configuration_utils import register_to_config
from diffusers.loaders import PeftAdapterMixin
from diffusers.models.modeling_utils import ModelMixin
from torch.backends.cuda import sdp_kernel
from torch.nn.attention.flex_attention import BlockMask
from torch.nn.attention.flex_attention import create_block_mask
from torch.nn.attention.flex_attention import flex_attention

from .attention import flash_attention


flex_attention = torch.compile(flex_attention, dynamic=False, mode="max-autotune")

DISABLE_COMPILE = False  # get os env

__all__ = ["WanModel"]


def sinusoidal_embedding_1d(dim, position):
    # preprocess
    assert dim % 2 == 0
    half = dim // 2
    position = position.type(torch.float64)

    # calculation
    sinusoid = torch.outer(position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
    x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
    return x


@amp.autocast("cuda", enabled=False)
def rope_params(max_seq_len, dim, theta=10000):
    assert dim % 2 == 0
    freqs = torch.outer(
        torch.arange(max_seq_len), 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim))
    )
    freqs = torch.polar(torch.ones_like(freqs), freqs)
    return freqs


@amp.autocast("cuda", enabled=False)
def rope_apply(x, grid_sizes, freqs):
    n, c = x.size(2), x.size(3) // 2
    bs = x.size(0)

    # split freqs
    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)

    # loop over samples
    f, h, w = grid_sizes.tolist()
    seq_len = f * h * w

    # precompute multipliers

    x = torch.view_as_complex(x.to(torch.float32).reshape(bs, seq_len, n, -1, 2))
    freqs_i = torch.cat(
        [
            freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
            freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
            freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1),
        ],
        dim=-1,
    ).reshape(seq_len, 1, -1)

    # apply rotary embedding
    x = torch.view_as_real(x * freqs_i).flatten(3)

    return x


@torch.compile(dynamic=True, disable=DISABLE_COMPILE)
def fast_rms_norm(x, weight, eps):
    x = x.float()
    x = x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + eps)
    x = x.type_as(x) * weight
    return x


class WanRMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.dim = dim
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
        """
        return fast_rms_norm(x, self.weight, self.eps)

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)


class WanLayerNorm(nn.LayerNorm):
    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)

    def forward(self, x):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
        """
        return super().forward(x)


class WanSelfAttention(nn.Module):
    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6):
        assert dim % num_heads == 0
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.eps = eps

        # layers
        self.q = nn.Linear(dim, dim)
        self.k = nn.Linear(dim, dim)
        self.v = nn.Linear(dim, dim)
        self.o = nn.Linear(dim, dim)
        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()

        self._flag_ar_attention = False

    def set_ar_attention(self):
        self._flag_ar_attention = True

    def forward(self, x, grid_sizes, freqs, block_mask):
        r"""
        Args:
            x(Tensor): Shape [B, L, num_heads, C / num_heads]
            seq_lens(Tensor): Shape [B]
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim

        # query, key, value function
        def qkv_fn(x):
            q = self.norm_q(self.q(x)).view(b, s, n, d)
            k = self.norm_k(self.k(x)).view(b, s, n, d)
            v = self.v(x).view(b, s, n, d)
            return q, k, v

        x = x.to(self.q.weight.dtype)
        q, k, v = qkv_fn(x)

        if not self._flag_ar_attention:
            q = rope_apply(q, grid_sizes, freqs)
            k = rope_apply(k, grid_sizes, freqs)
            x = flash_attention(q=q, k=k, v=v, window_size=self.window_size)
        else:
            q = rope_apply(q, grid_sizes, freqs)
            k = rope_apply(k, grid_sizes, freqs)
            q = q.to(torch.bfloat16)
            k = k.to(torch.bfloat16)
            v = v.to(torch.bfloat16)

            with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
                x = (
                    torch.nn.functional.scaled_dot_product_attention(
                        q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), attn_mask=block_mask
                    )
                    .transpose(1, 2)
                    .contiguous()
                )

        # output
        x = x.flatten(2)
        x = self.o(x)
        return x


class WanT2VCrossAttention(WanSelfAttention):
    def forward(self, x, context):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            context(Tensor): Shape [B, L2, C]
            context_lens(Tensor): Shape [B]
        """
        b, n, d = x.size(0), self.num_heads, self.head_dim

        # compute query, key, value
        q = self.norm_q(self.q(x)).view(b, -1, n, d)
        k = self.norm_k(self.k(context)).view(b, -1, n, d)
        v = self.v(context).view(b, -1, n, d)

        # compute attention
        x = flash_attention(q, k, v)

        # output
        x = x.flatten(2)
        x = self.o(x)
        return x


class WanI2VCrossAttention(WanSelfAttention):
    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6):
        super().__init__(dim, num_heads, window_size, qk_norm, eps)

        self.k_img = nn.Linear(dim, dim)
        self.v_img = nn.Linear(dim, dim)
        # self.alpha = nn.Parameter(torch.zeros((1, )))
        self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()

    def forward(self, x, context):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            context(Tensor): Shape [B, L2, C]
            context_lens(Tensor): Shape [B]
        """
        context_img = context[:, :257]
        context = context[:, 257:]
        b, n, d = x.size(0), self.num_heads, self.head_dim

        # compute query, key, value
        q = self.norm_q(self.q(x)).view(b, -1, n, d)
        k = self.norm_k(self.k(context)).view(b, -1, n, d)
        v = self.v(context).view(b, -1, n, d)
        k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
        v_img = self.v_img(context_img).view(b, -1, n, d)
        img_x = flash_attention(q, k_img, v_img)
        # compute attention
        x = flash_attention(q, k, v)

        # output
        x = x.flatten(2)
        img_x = img_x.flatten(2)
        x = x + img_x
        x = self.o(x)
        return x


WAN_CROSSATTENTION_CLASSES = {
    "t2v_cross_attn": WanT2VCrossAttention,
    "i2v_cross_attn": WanI2VCrossAttention,
}


def mul_add(x, y, z):
    return x.float() + y.float() * z.float()


def mul_add_add(x, y, z):
    return x.float() * (1 + y) + z


mul_add_compile = torch.compile(mul_add, dynamic=True, disable=DISABLE_COMPILE)
mul_add_add_compile = torch.compile(mul_add_add, dynamic=True, disable=DISABLE_COMPILE)


class WanAttentionBlock(nn.Module):
    def __init__(
        self,
        cross_attn_type,
        dim,
        ffn_dim,
        num_heads,
        window_size=(-1, -1),
        qk_norm=True,
        cross_attn_norm=False,
        eps=1e-6,
    ):
        super().__init__()
        self.dim = dim
        self.ffn_dim = ffn_dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.cross_attn_norm = cross_attn_norm
        self.eps = eps

        # layers
        self.norm1 = WanLayerNorm(dim, eps)
        self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
        self.norm3 = WanLayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
        self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, num_heads, (-1, -1), qk_norm, eps)
        self.norm2 = WanLayerNorm(dim, eps)
        self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(approximate="tanh"), nn.Linear(ffn_dim, dim))

        # modulation
        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)

    def set_ar_attention(self):
        self.self_attn.set_ar_attention()

    def forward(
        self,
        x,
        e,
        grid_sizes,
        freqs,
        context,
        block_mask,
    ):
        r"""
        Args:
            x(Tensor): Shape [B, L, C]
            e(Tensor): Shape [B, 6, C]
            seq_lens(Tensor): Shape [B], length of each sequence in batch
            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
        """
        if e.dim() == 3:
            modulation = self.modulation  # 1, 6, dim
            with amp.autocast("cuda", dtype=torch.float32):
                e = (modulation + e).chunk(6, dim=1)
        elif e.dim() == 4:
            modulation = self.modulation.unsqueeze(2)  # 1, 6, 1, dim
            with amp.autocast("cuda", dtype=torch.float32):
                e = (modulation + e).chunk(6, dim=1)
            e = [ei.squeeze(1) for ei in e]

        # self-attention
        out = mul_add_add_compile(self.norm1(x), e[1], e[0])
        y = self.self_attn(out, grid_sizes, freqs, block_mask)
        with amp.autocast("cuda", dtype=torch.float32):
            x = mul_add_compile(x, y, e[2])

        # cross-attention & ffn function
        def cross_attn_ffn(x, context, e):
            dtype = context.dtype
            x = x + self.cross_attn(self.norm3(x.to(dtype)), context)
            y = self.ffn(mul_add_add_compile(self.norm2(x), e[4], e[3]).to(dtype))
            with amp.autocast("cuda", dtype=torch.float32):
                x = mul_add_compile(x, y, e[5])
            return x

        x = cross_attn_ffn(x, context, e)
        return x.to(torch.bfloat16)


class Head(nn.Module):
    def __init__(self, dim, out_dim, patch_size, eps=1e-6):
        super().__init__()
        self.dim = dim
        self.out_dim = out_dim
        self.patch_size = patch_size
        self.eps = eps

        # layers
        out_dim = math.prod(patch_size) * out_dim
        self.norm = WanLayerNorm(dim, eps)
        self.head = nn.Linear(dim, out_dim)

        # modulation
        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)

    def forward(self, x, e):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
            e(Tensor): Shape [B, C]
        """
        with amp.autocast("cuda", dtype=torch.float32):
            if e.dim() == 2:
                modulation = self.modulation  # 1, 2, dim
                e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)

            elif e.dim() == 3:
                modulation = self.modulation.unsqueeze(2)  # 1, 2, seq, dim
                e = (modulation + e.unsqueeze(1)).chunk(2, dim=1)
                e = [ei.squeeze(1) for ei in e]
            x = self.head(self.norm(x) * (1 + e[1]) + e[0])
        return x


class MLPProj(torch.nn.Module):
    def __init__(self, in_dim, out_dim):
        super().__init__()

        self.proj = torch.nn.Sequential(
            torch.nn.LayerNorm(in_dim),
            torch.nn.Linear(in_dim, in_dim),
            torch.nn.GELU(),
            torch.nn.Linear(in_dim, out_dim),
            torch.nn.LayerNorm(out_dim),
        )

    def forward(self, image_embeds):
        clip_extra_context_tokens = self.proj(image_embeds)
        return clip_extra_context_tokens


class WanModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
    r"""
    Wan diffusion backbone supporting both text-to-video and image-to-video.
    """

    ignore_for_config = ["patch_size", "cross_attn_norm", "qk_norm", "text_dim", "window_size"]
    _no_split_modules = ["WanAttentionBlock"]

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        model_type="t2v",
        patch_size=(1, 2, 2),
        text_len=512,
        in_dim=16,
        dim=2048,
        ffn_dim=8192,
        freq_dim=256,
        text_dim=4096,
        out_dim=16,
        num_heads=16,
        num_layers=32,
        window_size=(-1, -1),
        qk_norm=True,
        cross_attn_norm=True,
        inject_sample_info=False,
        eps=1e-6,
    ):
        r"""
        Initialize the diffusion model backbone.

        Args:
            model_type (`str`, *optional*, defaults to 't2v'):
                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
            patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
                3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
            text_len (`int`, *optional*, defaults to 512):
                Fixed length for text embeddings
            in_dim (`int`, *optional*, defaults to 16):
                Input video channels (C_in)
            dim (`int`, *optional*, defaults to 2048):
                Hidden dimension of the transformer
            ffn_dim (`int`, *optional*, defaults to 8192):
                Intermediate dimension in feed-forward network
            freq_dim (`int`, *optional*, defaults to 256):
                Dimension for sinusoidal time embeddings
            text_dim (`int`, *optional*, defaults to 4096):
                Input dimension for text embeddings
            out_dim (`int`, *optional*, defaults to 16):
                Output video channels (C_out)
            num_heads (`int`, *optional*, defaults to 16):
                Number of attention heads
            num_layers (`int`, *optional*, defaults to 32):
                Number of transformer blocks
            window_size (`tuple`, *optional*, defaults to (-1, -1)):
                Window size for local attention (-1 indicates global attention)
            qk_norm (`bool`, *optional*, defaults to True):
                Enable query/key normalization
            cross_attn_norm (`bool`, *optional*, defaults to False):
                Enable cross-attention normalization
            eps (`float`, *optional*, defaults to 1e-6):
                Epsilon value for normalization layers
        """

        super().__init__()

        assert model_type in ["t2v", "i2v"]
        self.model_type = model_type

        self.patch_size = patch_size
        self.text_len = text_len
        self.in_dim = in_dim
        self.dim = dim
        self.ffn_dim = ffn_dim
        self.freq_dim = freq_dim
        self.text_dim = text_dim
        self.out_dim = out_dim
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.cross_attn_norm = cross_attn_norm
        self.eps = eps
        self.num_frame_per_block = 1
        self.flag_causal_attention = False
        self.block_mask = None
        self.enable_teacache = False

        # embeddings
        self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
        self.text_embedding = nn.Sequential(nn.Linear(text_dim, dim), nn.GELU(approximate="tanh"), nn.Linear(dim, dim))

        self.time_embedding = nn.Sequential(nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))

        if inject_sample_info:
            self.fps_embedding = nn.Embedding(2, dim)
            self.fps_projection = nn.Sequential(nn.Linear(dim, dim), nn.SiLU(), nn.Linear(dim, dim * 6))

        # blocks
        cross_attn_type = "t2v_cross_attn" if model_type == "t2v" else "i2v_cross_attn"
        self.blocks = nn.ModuleList(
            [
                WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps)
                for _ in range(num_layers)
            ]
        )

        # head
        self.head = Head(dim, out_dim, patch_size, eps)

        # buffers (don't use register_buffer otherwise dtype will be changed in to())
        assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
        d = dim // num_heads
        self.freqs = torch.cat(
            [rope_params(1024, d - 4 * (d // 6)), rope_params(1024, 2 * (d // 6)), rope_params(1024, 2 * (d // 6))],
            dim=1,
        )

        if model_type == "i2v":
            self.img_emb = MLPProj(1280, dim)

        self.gradient_checkpointing = False

        self.cpu_offloading = False

        self.inject_sample_info = inject_sample_info
        # initialize weights
        self.init_weights()

    def _set_gradient_checkpointing(self, module, value=False):
        self.gradient_checkpointing = value

    def zero_init_i2v_cross_attn(self):
        print("zero init i2v cross attn")
        for i in range(self.num_layers):
            self.blocks[i].cross_attn.v_img.weight.data.zero_()
            self.blocks[i].cross_attn.v_img.bias.data.zero_()

    @staticmethod
    def _prepare_blockwise_causal_attn_mask(
        device: torch.device | str, num_frames: int = 21, frame_seqlen: int = 1560, num_frame_per_block=1
    ) -> BlockMask:
        """
        we will divide the token sequence into the following format
        [1 latent frame] [1 latent frame] ... [1 latent frame]
        We use flexattention to construct the attention mask
        """
        total_length = num_frames * frame_seqlen

        # we do right padding to get to a multiple of 128
        padded_length = math.ceil(total_length / 128) * 128 - total_length

        ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)

        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
        frame_indices = torch.arange(start=0, end=total_length, step=frame_seqlen * num_frame_per_block, device=device)

        for tmp in frame_indices:
            ends[tmp : tmp + frame_seqlen * num_frame_per_block] = tmp + frame_seqlen * num_frame_per_block

        def attention_mask(b, h, q_idx, kv_idx):
            return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
            # return ((kv_idx < total_length) & (q_idx < total_length))  | (q_idx == kv_idx) # bidirectional mask

        block_mask = create_block_mask(
            attention_mask,
            B=None,
            H=None,
            Q_LEN=total_length + padded_length,
            KV_LEN=total_length + padded_length,
            _compile=False,
            device=device,
        )

        return block_mask

    def initialize_teacache(self, enable_teacache=True, num_steps=25, teacache_thresh=0.15, use_ret_steps=False, ckpt_dir=''):
        self.enable_teacache = enable_teacache
        print('using teacache')
        self.cnt = 0
        self.num_steps = num_steps
        self.teacache_thresh = teacache_thresh
        self.accumulated_rel_l1_distance_even = 0
        self.accumulated_rel_l1_distance_odd = 0
        self.previous_e0_even = None
        self.previous_e0_odd = None
        self.previous_residual_even = None
        self.previous_residual_odd = None
        self.use_ref_steps = use_ret_steps
        if "I2V" in ckpt_dir:
            if use_ret_steps:
                if '540P' in ckpt_dir:
                    self.coefficients = [ 2.57151496e+05, -3.54229917e+04,  1.40286849e+03, -1.35890334e+01, 1.32517977e-01]
                if '720P' in ckpt_dir:
                    self.coefficients = [ 8.10705460e+03,  2.13393892e+03, -3.72934672e+02,  1.66203073e+01, -4.17769401e-02]
                self.ret_steps = 5*2
                self.cutoff_steps = num_steps*2
            else:
                if '540P' in ckpt_dir:
                    self.coefficients = [-3.02331670e+02,  2.23948934e+02, -5.25463970e+01,  5.87348440e+00, -2.01973289e-01]
                if '720P' in ckpt_dir:
                    self.coefficients = [-114.36346466,   65.26524496,  -18.82220707,    4.91518089,   -0.23412683]
                self.ret_steps = 1*2
                self.cutoff_steps = num_steps*2 - 2
        else:
            if use_ret_steps:
                if '1.3B' in ckpt_dir:
                    self.coefficients = [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02]
                if '14B' in ckpt_dir:
                    self.coefficients = [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01]
                self.ret_steps = 5*2
                self.cutoff_steps = num_steps*2
            else:
                if '1.3B' in ckpt_dir:
                    self.coefficients = [2.39676752e+03, -1.31110545e+03,  2.01331979e+02, -8.29855975e+00, 1.37887774e-01]
                if '14B' in ckpt_dir:
                    self.coefficients = [-5784.54975374,  5449.50911966, -1811.16591783,   256.27178429, -13.02252404]
                self.ret_steps = 1*2
                self.cutoff_steps = num_steps*2 - 2

    def forward(self, x, t, context, clip_fea=None, y=None, fps=None):
        r"""
        Forward pass through the diffusion model

        Args:
            x (List[Tensor]):
                List of input video tensors, each with shape [C_in, F, H, W]
            t (Tensor):
                Diffusion timesteps tensor of shape [B]
            context (List[Tensor]):
                List of text embeddings each with shape [L, C]
            seq_len (`int`):
                Maximum sequence length for positional encoding
            clip_fea (Tensor, *optional*):
                CLIP image features for image-to-video mode
            y (List[Tensor], *optional*):
                Conditional video inputs for image-to-video mode, same shape as x

        Returns:
            List[Tensor]:
                List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
        """
        if self.model_type == "i2v":
            assert clip_fea is not None and y is not None
        # params
        device = self.patch_embedding.weight.device
        if self.freqs.device != device:
            self.freqs = self.freqs.to(device)

        if y is not None:
            x = torch.cat([x, y], dim=1)

        # embeddings
        x = self.patch_embedding(x)
        grid_sizes = torch.tensor(x.shape[2:], dtype=torch.long)
        x = x.flatten(2).transpose(1, 2)

        if self.flag_causal_attention:
            frame_num = grid_sizes[0]
            height = grid_sizes[1]
            width = grid_sizes[2]
            block_num = frame_num // self.num_frame_per_block
            range_tensor = torch.arange(block_num).view(-1, 1)
            range_tensor = range_tensor.repeat(1, self.num_frame_per_block).flatten()
            casual_mask = range_tensor.unsqueeze(0) <= range_tensor.unsqueeze(1)  # f, f
            casual_mask = casual_mask.view(frame_num, 1, 1, frame_num, 1, 1).to(x.device)
            casual_mask = casual_mask.repeat(1, height, width, 1, height, width)
            casual_mask = casual_mask.reshape(frame_num * height * width, frame_num * height * width)
            self.block_mask = casual_mask.unsqueeze(0).unsqueeze(0)

        # time embeddings
        with amp.autocast("cuda", dtype=torch.float32):
            if t.dim() == 2:
                b, f = t.shape
                _flag_df = True
            else:
                _flag_df = False

            e = self.time_embedding(
                sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(self.patch_embedding.weight.dtype)
            )  # b, dim
            e0 = self.time_projection(e).unflatten(1, (6, self.dim))  # b, 6, dim

            if self.inject_sample_info:
                fps = torch.tensor(fps, dtype=torch.long, device=device)

                fps_emb = self.fps_embedding(fps).float()
                if _flag_df:
                    e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)).repeat(t.shape[1], 1, 1)
                else:
                    e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim))

            if _flag_df:
                e = e.view(b, f, 1, 1, self.dim)
                e0 = e0.view(b, f, 1, 1, 6, self.dim)
                e = e.repeat(1, 1, grid_sizes[1], grid_sizes[2], 1).flatten(1, 3)
                e0 = e0.repeat(1, 1, grid_sizes[1], grid_sizes[2], 1, 1).flatten(1, 3)
                e0 = e0.transpose(1, 2).contiguous()

            assert e.dtype == torch.float32 and e0.dtype == torch.float32

        # context
        context = self.text_embedding(context)

        if clip_fea is not None:
            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
            context = torch.concat([context_clip, context], dim=1)

        # arguments
        kwargs = dict(e=e0, grid_sizes=grid_sizes, freqs=self.freqs, context=context, block_mask=self.block_mask)
        if self.enable_teacache:
            modulated_inp = e0 if self.use_ref_steps else e
            # teacache
            if self.cnt%2==0: # even -> conditon
                self.is_even = True
                if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:
                    should_calc_even = True
                    self.accumulated_rel_l1_distance_even = 0
                else:
                    rescale_func = np.poly1d(self.coefficients)
                    self.accumulated_rel_l1_distance_even += rescale_func(((modulated_inp-self.previous_e0_even).abs().mean() / self.previous_e0_even.abs().mean()).cpu().item())
                    if self.accumulated_rel_l1_distance_even < self.teacache_thresh:
                        should_calc_even = False
                    else:
                        should_calc_even = True
                        self.accumulated_rel_l1_distance_even = 0
                self.previous_e0_even = modulated_inp.clone()

            else: # odd -> unconditon
                self.is_even = False
                if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:
                    should_calc_odd = True
                    self.accumulated_rel_l1_distance_odd = 0
                else: 
                    rescale_func = np.poly1d(self.coefficients)
                    self.accumulated_rel_l1_distance_odd += rescale_func(((modulated_inp-self.previous_e0_odd).abs().mean() / self.previous_e0_odd.abs().mean()).cpu().item())
                    if self.accumulated_rel_l1_distance_odd < self.teacache_thresh:
                        should_calc_odd = False
                    else:
                        should_calc_odd = True
                        self.accumulated_rel_l1_distance_odd = 0
                self.previous_e0_odd = modulated_inp.clone()

        if self.enable_teacache: 
            if self.is_even:
                if not should_calc_even:
                    x += self.previous_residual_even
                else:
                    ori_x = x.clone()
                    for block in self.blocks:
                        x = block(x, **kwargs)
                    self.previous_residual_even = x - ori_x
            else:
                if not should_calc_odd:
                    x += self.previous_residual_odd
                else:
                    ori_x = x.clone()
                    for block in self.blocks:
                        x = block(x, **kwargs)
                    self.previous_residual_odd = x - ori_x

            self.cnt += 1
            if self.cnt >= self.num_steps:
                self.cnt = 0
        else:
            for block in self.blocks:
                x = block(x, **kwargs)

        x = self.head(x, e)

        # unpatchify
        x = self.unpatchify(x, grid_sizes)

        return x.float()

    def unpatchify(self, x, grid_sizes):
        r"""
        Reconstruct video tensors from patch embeddings.

        Args:
            x (List[Tensor]):
                List of patchified features, each with shape [L, C_out * prod(patch_size)]
            grid_sizes (Tensor):
                Original spatial-temporal grid dimensions before patching,
                    shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)

        Returns:
            List[Tensor]:
                Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
        """

        c = self.out_dim
        bs = x.shape[0]
        x = x.view(bs, *grid_sizes, *self.patch_size, c)
        x = torch.einsum("bfhwpqrc->bcfphqwr", x)
        x = x.reshape(bs, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])

        return x

    def set_ar_attention(self, causal_block_size):
        self.num_frame_per_block = causal_block_size
        self.flag_causal_attention = True
        for block in self.blocks:
            block.set_ar_attention()

    def init_weights(self):
        r"""
        Initialize model parameters using Xavier initialization.
        """

        # basic init
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)

        # init embeddings
        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
        for m in self.text_embedding.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, std=0.02)
        for m in self.time_embedding.modules():
            if isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, std=0.02)

        if self.inject_sample_info:
            nn.init.normal_(self.fps_embedding.weight, std=0.02)

            for m in self.fps_projection.modules():
                if isinstance(m, nn.Linear):
                    nn.init.normal_(m.weight, std=0.02)

            nn.init.zeros_(self.fps_projection[-1].weight)
            nn.init.zeros_(self.fps_projection[-1].bias)

        # init output layer
        nn.init.zeros_(self.head.head.weight)